import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision  # 数据库模块
import matplotlib.pyplot as plt

torch.manual_seed(1)  # reproducible

# Hyper Parameters
EPOCH = 1  # 训练整批数据多少次, 为了节约时间, 我们只训练一次
BATCH_SIZE = 50
LR = 0.001  # 学习率
DOWNLOAD_MNIST = True  # 如果你已经下载好了mnist数据就写上 False

# 训练数据
# Mnist 手写数字
train_data = torchvision.datasets.MNIST(
    root='./mnist/',  # 保存或者提取位置
    train=True,  # this is training data
    transform=torchvision.transforms.ToTensor(),  # 转换 PIL.Image or numpy.ndarray 成
    # torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
    download=DOWNLOAD_MNIST,  # 没下载就下载, 下载了就不用再下了
)

# 测试数据
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)

# 批训练 50samples, 1 channel, 28x28 (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# 为了节约时间, 我们测试时只测试前2000个
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[
         :2000] / 255.  # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]


class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(  # input shape (1, 28, 28)
            nn.Conv2d(
                in_channels=1,  # input height
                out_channels=16,  # n_filters
                kernel_size=5,  # filter size
                stride=1,  # filter movement/step
                padding=2,  # 如果想要 con2d 出来的图片长宽没有变化, padding=(kernel_size-1)/2 当 stride=1
            ),  # output shape (16, 28, 28)
            nn.ReLU(),  # activation
            nn.MaxPool2d(kernel_size=2),  # 在 2x2 空间里向下采样, output shape (16, 14, 14)
        )
        self.conv2 = nn.Sequential(  # input shape (16, 14, 14)
            nn.Conv2d(16, 32, 5, 1, 2),  # output shape (32, 14, 14)
            nn.ReLU(),  # activation
            nn.MaxPool2d(2),  # output shape (32, 7, 7)
        )
        self.out = nn.Linear(32 * 7 * 7, 10)  # fully connected layer, output 10 classes

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)  # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
        output = self.out(x)
        return output


cnn = CNN()
# print(cnn)  # net architecture

optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)  # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()  # the target label is not one-hotted

print(test_y[:10].numpy(), 'real number')
# training and testing
for epoch in range(EPOCH):
    for step, (b_x, b_y) in enumerate(train_loader):  # 分配 batch data, normalize x when iterate train_loader
        output = cnn(b_x)  # cnn output
        loss = loss_func(output, b_y)  # cross entropy loss
        optimizer.zero_grad()  # clear gradients for this training step
        loss.backward()  # backpropagation, compute gradients
        optimizer.step()  # apply gradients
        if step % 100 == 0:  # 每5000个sample打印
            test_output = cnn(test_x[:10])
            pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
            print(pred_y, 'prediction number')

from PIL import Image

for x in range(10):
    num = test_x[x].numpy() * 256
    img_b = Image.new("RGB", (28, 28))  # 不指定color，则为黑色#000000
    for i in range(28):
        for j in range(28):
            img_b.putpixel((j, i), (num[0][i][j], num[0][i][j], num[0][i][j]))

    plt.imshow(img_b)
    plt.show()
